retrieval benchmark
MTEB English v2 Retrieval is a retrieval benchmark based on the current MTEB(eng, v2) retrieval leaderboard.
Updated Sep 8, 2026
Higher official leaderboard task-type mean ranks better on this benchmark.
Rank | Model | Official leaderboard task-type mean | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelIEYuan-embedding-2.0-enIeityuan | Official leaderboard task-type mean70.69% | Percentile100.00% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank02 | ModelJCingot-8b-r3Jcorners | Official leaderboard task-type mean70.00% | Percentile99.53% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank03 | ModelAC | Official leaderboard task-type mean69.44% | Percentile99.05% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank04 | ModelGAgeevec-embeddings-1.0Geevec Ai | Official leaderboard task-type mean68.76% | Percentile98.58% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank05 | ModelAC | Official leaderboard task-type mean68.46% | Percentile98.10% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank06 | ModelBSSeed1.5-EmbeddingBytedance Seed | Official leaderboard task-type mean67.45% | Percentile97.63% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank07 | ModelKLQZhou-EmbeddingKingsoft Llm | Official leaderboard task-type mean67.12% | Percentile97.16% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank08 | ModelINJasper-Token-Compression-600MInfgrad | Official leaderboard task-type mean66.19% | Percentile96.68% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank09 | ModelANLGAI-Embedding-PreviewAnnamodels | Official leaderboard task-type mean66.18% | Percentile96.21% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank10 | ModelBY | Official leaderboard task-type mean64.90% | Percentile95.73% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank11 | ModelGO | Official leaderboard task-type mean64.35% | Percentile95.26% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank12 | ModelINinf-retriever-v1Infly | Official leaderboard task-type mean64.07% | Percentile94.79% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank13 | ModelNV | Official leaderboard task-type mean62.84% | Percentile94.31% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank14 | ModelGAgeevec-embeddings-1.0-liteGeevec Ai | Official leaderboard task-type mean62.23% | Percentile93.84% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank15 | ModelAC | Official leaderboard task-type mean61.83% | Percentile93.36% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank16 | ModelGRGeoEmbeddingGeogpt Research Project | Official leaderboard task-type mean60.91% | Percentile92.89% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank17 | ModelINinf-retriever-v1-1.5bInfly | Official leaderboard task-type mean60.83% | Percentile92.42% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank18 | ModelCAF2LLM-v2-14BCodefuse Ai | Official leaderboard task-type mean60.63% | Percentile91.94% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank19 | ModelLALinq-Embed-MistralLinq Ai Research | Official leaderboard task-type mean60.14% | Percentile91.47% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank20 | ModelNV | Official leaderboard task-type mean60.13% | Percentile91.00% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank21 | ModelJIjina-embeddings-v5-omni-smallJinaai | Official leaderboard task-type mean60.07% | Percentile90.52% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank22 | ModelJIjina-embeddings-v5-text-smallJinaai | Official leaderboard task-type mean60.07% | Percentile90.05% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank23 | ModelASGiga-Embeddings-instruct-10B-A1.8B-0826Ai Sage | Official leaderboard task-type mean60.03% | Percentile89.57% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank24 | ModelCAF2LLM-v2-8BCodefuse Ai | Official leaderboard task-type mean59.82% | Percentile89.10% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank25 | ModelASGiga-Embeddings-instructAi Sage | Official leaderboard task-type mean59.68% | Percentile88.63% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank26 | ModelCAF2LLM-4BCodefuse Ai | Official leaderboard task-type mean59.63% | Percentile88.15% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank27 | ModelASGiga-Embeddings-instruct-3B-0826Ai Sage | Official leaderboard task-type mean59.45% | Percentile87.68% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank28 | ModelITICT-TIME-and-Querit-embedding-v1Ict Time And Querit | Official leaderboard task-type mean59.37% | Percentile87.20% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank29 | ModelSASFR-Embedding-MistralSalesforce | Official leaderboard task-type mean59.33% | Percentile86.73% | Participants212 | EvidenceA | EvaluatedN/A |
| Rank30 | ModelCAF2LLM-v2-4BCodefuse Ai | Official leaderboard task-type mean59.13% | Percentile86.26% | Participants212 | EvidenceA | EvaluatedN/A |
The leading models and scores on this benchmark.
A closer view of the leading scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
What MTEB English v2 Retrieval measures and how its scores work.
MTEB English v2 Retrieval is the retrieval task category of MTEB(eng, v2).
It reports the Official leaderboard task-type mean in ratio units.
Scores are shown in ratio. This benchmark is verified and has an evidence level of A.
MTEB. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about MTEB English v2 Retrieval.
Yuan-embedding-2.0-en is currently ranked first with 70.69%.
The current leaders are Yuan-embedding-2.0-en (70.69%), ingot-8b-r3 (70.00%), and Qwen3-Embedding-8B (69.44%).
Gemini Embedding 001 has the lowest matched official input price at $0.15 input per 1M tokens.
No matched runtime record is currently available.
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It reports the Official leaderboard task-type mean in ratio units.
Yes. Higher values rank better for this benchmark.
100 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.
Ranking basisThis mteb english v2 retrieval AI model leaderboard uses descending official leaderboard task-type mean in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Yuan-embedding-2.0-en currently leads MTEB English v2 Retrieval with 70.69%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.